Optimal tool selection for 2.5D milling, part 1: a solid-modeling approach for construction of the Voronoi mountain

Green manufacturing (GM in short) is beneficial to the alleviation of environment burdens. The optimization selection of tool is an important approach to improving environmental performance of cutting machining. The objective factors of decision-making problems for traditional tool selection are time, quality and cost. Based on the main idea of environmental consciousness, a decision-making framework model of GM is proposed; a multi-objective decision-making model for cutting tool selection is put forward. The objectives include Time (T), Quality (Q), Cost (C), Resources (R) and Environmental impact (E), where T aims to minimize the produce time, Q means to maximize the quality, C means to minimize the cost, R means to minimize the resource consumption and E means to minimize the environment impact respectively. Each objective is analyzed in detail and application of the Fuzzy Analysis Algorithm in the decision-making is discussed. A case study in which a practical decision-making problem of cutting tool selection for GM is analyzed and successful application of the above model shows that the model is practical.

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Design of the CNC Milling Tool Selection System

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.148-149.153 ◽

2011 ◽

Vol 148-149 ◽

pp. 153-157

Author(s):

Xiu Lin Sui ◽

Na Hu ◽

Chun Hong Zhang

Keyword(s):

Selection Process ◽

Complex Surface ◽

Forward Direction ◽

Computer Applications ◽

Cnc Milling ◽

Tool Selection ◽

Milling Tool ◽

Selection For ◽

Inference Strategy ◽

Reasoning Mechanism

Knowledge base of milling feature machining based on relational database is proposed, using knowledge representation of production rules, according to the characteristics of feature machining knowledge. Tool selected reasoning mechanism and reasoning processes is presented basing the installed CNC milling tool database , and further reasoning is based on knowledge, milling tool selection method is implemented based on feature machining knowledge using the forward direction inference strategy .In the paper, a complete system of the selecting milling cutter is established. The system connects not only the theoretical knowledge but the expert’s experiences with the computer applications in order to provide a base of realizing the automatic mechanical processing. By the example of machining tool selection for complex surface, the selection process is described, and the system can select the tools to meet the processing requirements within a shot time, and has good versatility.

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Tool selection for optimal part production: a Lagrangian relaxation approach

IIE Transactions ◽

10.1080/07408179508936758 ◽

1995 ◽

Vol 27 (4) ◽

pp. 417-426 ◽

Cited By ~ 13

Author(s):

Vernon Ning Hsu ◽

Mark Daskin ◽

Philip C. Jones ◽

Timothy J. Lowe

Keyword(s):

Lagrangian Relaxation ◽

Tool Selection ◽

Selection For ◽

Part Production

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Systematic Direct Solid Modeling Approach for Surface Micromachined MEMS

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.433-440.3130 ◽

2012 ◽

Vol 433-440 ◽

pp. 3130-3137 ◽

Cited By ~ 1

Author(s):

Jian Hua Li ◽

Yu Sheng Liu ◽

Hao Ling ◽

Wei Bin Guo ◽

Gao Qi He

Keyword(s):

Local Variation ◽

Solid Modeling ◽

Design Activity ◽

Creative Design ◽

Mems Devices ◽

Layer Model ◽

Modeling Approach ◽

Practical Model ◽

Mems Device ◽

Direct Solid

Current MEMS design methods do not fulfill the needs of emerging complex MEMS devices. In this paper, a systematic direct solid modeling approach for surface micromachined MEMS design is proposed. In this approach, practical model of a surface micromachined MEMS device, designed in a traditional CAD environment, is simplified firstly; after simplification, masks and process sequences are generated through solid-based mask synthesis; then local variation is used to refining the 3D layer model; finally masks and process sequences are verified in rough simulation and accurate simulation. The approach aims at enabling designers to focus on creative design activity in an intuitive mode.

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TOPSIS Multi-Criteria Decision Modeling Approach for Biolubricant Selection for Two-Stroke Petrol Engines

Energies ◽

10.3390/en81212408 ◽

2015 ◽

Vol 8 (12) ◽

pp. 13960-13970 ◽

Cited By ~ 9

Author(s):

Masoud Dehghani Soufi ◽

Barat Ghobadian ◽

Gholamhassan Najafi ◽

Mohammad Sabzimaleki ◽

Talal Yusaf

Keyword(s):

Decision Modeling ◽

Modeling Approach ◽

Selection For

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Intelligent Tool Selection for Machining Cylindrical Components: Part 1: Logic of the Knowledge-Based Module

Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture ◽

10.1243/pime_proc_1995_209_071_02 ◽

1995 ◽

Vol 209 (3) ◽

pp. 173-182 ◽

Cited By ~ 7

Author(s):

P G Maropoulos ◽

P A T Gill

Keyword(s):

Metal Cutting ◽

Testing Procedure ◽

Similarity Criteria ◽

Machining Performance ◽

Tool Selection ◽

Knowledge Based ◽

Operation Rules ◽

Practical Engineering ◽

Selection For ◽

Definition Of

This paper presents the logic of a knowledge-based system for turning tool selection. The selection philosophy is based on machining performance and the system uses information regarding tools and cutting data from ‘approved’ operations which have been proved on machine tools following a specific testing procedure. For any new operation, rules are used to identify its level of similarity to previously performed, approved operations. The similarity criteria are based on metal cutting theory and practical engineering knowledge and incorporate considerations in relation to the component and cutting profile geometry, material type and operation type as well as tool and insert characteristics. The goal is to identify similar approved operations, retrieve the corresponding tool and cutting data and sort them in order of preference. A key function of the system is that according to the level of similarity, the retrieved information is either used as it is or is automatically modified to suit the new operation. The main benefits from using the system are improved engineering consistency in the decision making for selecting tools and cutting conditions, improved utilization of tools and definition of efficient machining conditions.

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